Text Image Deblurring Using Kernel Sparsity Prior.
IEEE Trans Cybern
; 50(3): 997-1008, 2020 Mar.
Article
em En
| MEDLINE
| ID: mdl-30403647
ABSTRACT
Previous methods on text image motion deblurring seldom consider the sparse characteristics of the blur kernel. This paper proposes a new text image motion deblurring method by exploiting the sparse properties of both text image itself and kernel. It incorporates the L 0 -norm for regularizing the blur kernel in the deblurring model, besides the L 0 sparse priors for the text image and its gradient. Such a L 0 -norm-based model is efficiently optimized by half-quadratic splitting coupled with the fast conjugate descent method. To further improve the quality of the recovered kernel, a structure-preserving kernel denoising method is also developed to filter out the noisy pixels, yielding a clean kernel curve. Experimental results show the superiority of the proposed method. The source code and results are available at https//github.com/shenjianbing/text-image-deblur.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
IEEE Trans Cybern
Ano de publicação:
2020
Tipo de documento:
Article
País de publicação:
EEUU
/
ESTADOS UNIDOS
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ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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US
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USA